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<p>The present study examines association between deepfake content trustworthiness, perceived importance of content, attitude towards content, and behavioral intentions. Furthermore, the mediating role of perceived importance of content and moderating role of attitude is also observed. The data is collected from 393 customers approached in shopping malls in Delhi-NCR through mall-intercept survey methodology and proposed theoretical model is tested using PLS-SEM. The results indicate that the trustworthiness of deepfake content significantly influence individual’s attitude and behavioral intention. The perceived importance of content also plays significant roles as driving factor and mediator, sharping the way users engage with deepfake media. This study will offer insights to practitioners, media platforms and digital content creators in addressing misinformation and enhancing audience trust. This study also provides valuable perspective on elaboration likelihood model that how content evaluation is carried out by users in the age of AI-generated media.</p>

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Decoding Deepfake Perceptions: Trustworthiness, Importance and Behavioral Intention

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • Vinod Kumar,
  • Sachin Kumar,
  • Sheshadri Chatterjee,
  • Ranjan Chaudhuri,
  • Shivam Gupta

摘要

The present study examines association between deepfake content trustworthiness, perceived importance of content, attitude towards content, and behavioral intentions. Furthermore, the mediating role of perceived importance of content and moderating role of attitude is also observed. The data is collected from 393 customers approached in shopping malls in Delhi-NCR through mall-intercept survey methodology and proposed theoretical model is tested using PLS-SEM. The results indicate that the trustworthiness of deepfake content significantly influence individual’s attitude and behavioral intention. The perceived importance of content also plays significant roles as driving factor and mediator, sharping the way users engage with deepfake media. This study will offer insights to practitioners, media platforms and digital content creators in addressing misinformation and enhancing audience trust. This study also provides valuable perspective on elaboration likelihood model that how content evaluation is carried out by users in the age of AI-generated media.